Dynamic bandwidth allocation for 3G wireless systems-A fuzzy approach

Authors:
S. Chandramathi;S. P. P. Raghuram;V. S. Srinivas;H. Satyajit Singh
Affiliations:
Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry 605014, India;Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry 605014, India;Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry 605014, India;Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry 605014, India
Venue:
Applied Soft Computing
Year:
2008

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Abstract

3G Wireless systems are to support multiple classes of traffic with widely different characteristics and quality of service (QoS) requirements. A major challenge in this system is to guarantee the promised QoS for the admitted users, while maximizing the resource allocation through dynamic resource sharing. In the case of multimedia call, each of the services has its own distinct QoS requirements concerning probability of blocking (P"B), service access delay (SAD), and access delay variation (ADV). The 3G wireless system attempts to deliver the required QoS by allocating appropriate resources (e.g. bandwidth, buffers), and bandwidth allocation is a key in achieving this. Dynamic bandwidth allocation policies reported so far in the literature deal with audio source only. They do not consider QoS requirements. In this work, a fuzzy logic (FL)-based dynamic bandwidth allocation algorithm for multimedia services with multiple QoS (P"B, SAD, ADV, and the arrival rate) requirements are presented and analyzed. Here, each service can declare a range of acceptable QoS levels (e.g. high, medium, and low). As QoS demand varies, the proposed algorithm allocates the best possible bandwidth to each of the services. This maximizes the utilization and fair distribution of resources. The proposed allocation method is validated in a variety of scenarios. The results show that the required QoS can be obtained by appropriately tuning the fuzzy logic controller (FLC).